Zobrazeno 1 - 10
of 826
pro vyhledávání: '"LIU Shuming"'
Publikováno v:
电力工程技术, Vol 42, Iss 2, Pp 188-196 (2023)
Lightning is one of the main causes of voltage sags in power grid. Accurate estimation of the severity of voltage sags caused by lightning can provide a basis for developing optimal management plans and siting sensitive users. In this paper, a data-d
Externí odkaz:
https://doaj.org/article/1646a1ef579d4a9e8c781e757b60f80e
As a fundamental task in long-form video understanding, temporal action detection (TAD) aims to capture inherent temporal relations in untrimmed videos and identify candidate actions with precise boundaries. Over the years, various networks, includin
Externí odkaz:
http://arxiv.org/abs/2407.17792
Masked AutoEncoders (MAE) have emerged as a robust self-supervised framework, offering remarkable performance across a wide range of downstream tasks. To increase the difficulty of the pretext task and learn richer visual representations, existing wo
Externí odkaz:
http://arxiv.org/abs/2407.13036
Autor:
Zhao, Chen, Liu, Shuming, Mangalam, Karttikeya, Qian, Guocheng, Zohra, Fatimah, Alghannam, Abdulmohsen, Malik, Jitendra, Ghanem, Bernard
Publikováno v:
the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024
Large pretrained models are increasingly crucial in modern computer vision tasks. These models are typically used in downstream tasks by end-to-end finetuning, which is highly memory-intensive for tasks with high-resolution data, e.g., video understa
Externí odkaz:
http://arxiv.org/abs/2401.04105
Recently, temporal action detection (TAD) has seen significant performance improvement with end-to-end training. However, due to the memory bottleneck, only models with limited scales and limited data volumes can afford end-to-end training, which ine
Externí odkaz:
http://arxiv.org/abs/2311.17241
Autor:
Zhuge, Mingchen, Liu, Haozhe, Faccio, Francesco, Ashley, Dylan R., Csordás, Róbert, Gopalakrishnan, Anand, Hamdi, Abdullah, Hammoud, Hasan Abed Al Kader, Herrmann, Vincent, Irie, Kazuki, Kirsch, Louis, Li, Bing, Li, Guohao, Liu, Shuming, Mai, Jinjie, Piękos, Piotr, Ramesh, Aditya, Schlag, Imanol, Shi, Weimin, Stanić, Aleksandar, Wang, Wenyi, Wang, Yuhui, Xu, Mengmeng, Fan, Deng-Ping, Ghanem, Bernard, Schmidhuber, Jürgen
Both Minsky's "society of mind" and Schmidhuber's "learning to think" inspire diverse societies of large multimodal neural networks (NNs) that solve problems by interviewing each other in a "mindstorm." Recent implementations of NN-based societies of
Externí odkaz:
http://arxiv.org/abs/2305.17066
Autor:
Xu, Mengmeng, Soldan, Mattia, Gao, Jialin, Liu, Shuming, Pérez-Rúa, Juan-Manuel, Ghanem, Bernard
Video activity localization aims at understanding the semantic content in long untrimmed videos and retrieving actions of interest. The retrieved action with its start and end locations can be used for highlight generation, temporal action detection,
Externí odkaz:
http://arxiv.org/abs/2304.02934
Publikováno v:
MATEC Web of Conferences, Vol 277, p 03009 (2019)
The paper takes the Chinese standard GB/T 14549-1993, the British Engineering Recommendation G5/4-1, the Institute of Electrical and Electronics Engineers IEEE Std 519-2014 and the part of IEC 61000-3 series standard as an example. Then summarize the
Externí odkaz:
https://doaj.org/article/e4849f3ca5404ef9aeec790027b52fbc
Autor:
Hammoud, Hasan Abed Al Kader, Liu, Shuming, Alkhrashi, Mohammed, AlBalawi, Fahad, Ghanem, Bernard
Deep neural networks (DNNs) are vulnerable to a class of attacks called "backdoor attacks", which create an association between a backdoor trigger and a target label the attacker is interested in exploiting. A backdoored DNN performs well on clean te
Externí odkaz:
http://arxiv.org/abs/2301.00986
Temporal action localization (TAL) requires long-form reasoning to predict actions of various durations and complex content. Given limited GPU memory, training TAL end to end (i.e., from videos to predictions) on long videos is a significant challeng
Externí odkaz:
http://arxiv.org/abs/2211.14053